Documentation Index
Fetch the complete documentation index at: https://docs.fenra.io/llms.txt
Use this file to discover all available pages before exploring further.
This guide covers how to send OpenAI usage data to Fenra.
Chat Completions (GPT-4, GPT-3.5, o1, o3)
import OpenAI from 'openai';
const openai = new OpenAI();
async function chat(messages) {
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages
});
// Send to Fenra
await fetch('https://ingest.fenra.io/usage/transactions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-Api-Key': process.env.FENRA_API_KEY
},
body: JSON.stringify({
provider: 'openai',
model: response.model,
usage: [{
type: 'tokens',
metrics: {
input_tokens: response.usage.prompt_tokens,
output_tokens: response.usage.completion_tokens,
total_tokens: response.usage.total_tokens
}
}],
context: {
billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
}
})
});
return response;
}
Prompt Caching
When the response includes prompt_tokens_details.cached_tokens, include it:
usage: [{
type: 'tokens',
metrics: {
input_tokens: response.usage.prompt_tokens,
output_tokens: response.usage.completion_tokens,
total_tokens: response.usage.total_tokens,
cached_tokens: response.usage.prompt_tokens_details?.cached_tokens || 0
}
}]
Reasoning Models (o1, o3)
OpenAI’s reasoning models include reasoning_tokens. Include them for accurate cost tracking:
usage: [{
type: 'tokens',
metrics: {
input_tokens: response.usage.prompt_tokens,
output_tokens: response.usage.completion_tokens,
total_tokens: response.usage.total_tokens,
reasoning_tokens: response.usage.completion_tokens_details?.reasoning_tokens || 0
}
}]
Batch and Priority Tiers
OpenAI offers different pricing tiers. Specify the tier using model_tier:
{
provider: 'openai',
model: 'gpt-4o',
model_tier: 'batch', // Options: 'batch', 'priority', 'flex', 'standard'
usage: [{
type: 'tokens',
metrics: {
input_tokens: response.usage.prompt_tokens,
output_tokens: response.usage.completion_tokens,
total_tokens: response.usage.total_tokens
}
}],
context: {
billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
}
}
Multimodal Tokens (Realtime API)
For realtime and multimodal models, track separate token types:
usage: [{
type: 'tokens',
metrics: {
input_tokens: response.usage.prompt_tokens,
output_tokens: response.usage.completion_tokens,
total_tokens: response.usage.total_tokens,
text_tokens: response.usage.prompt_tokens_details?.text_tokens || 0,
audio_tokens: response.usage.prompt_tokens_details?.audio_tokens || 0,
image_tokens: response.usage.prompt_tokens_details?.image_tokens || 0
}
}]
Image Generation (DALL-E)
const response = await openai.images.generate({
model: 'dall-e-3',
prompt: 'A sunset over mountains',
n: 1,
size: '1024x1024',
quality: 'hd'
});
await fetch('https://ingest.fenra.io/usage/transactions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-Api-Key': process.env.FENRA_API_KEY
},
body: JSON.stringify({
provider: 'openai',
model: 'dall-e-3',
usage: [{
type: 'images',
metrics: {
generated: response.data.length,
size_px: 1024,
quality: 'hd' // Options: 'standard', 'hd' for DALL-E
}
}],
context: {
billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
}
})
});
GPT Image Models
For GPT Image 1.5 and GPT Image 1, include quality and dimensions:
const response = await openai.images.generate({
model: 'gpt-image-1.5',
prompt: 'A futuristic city',
quality: 'high',
size: '1024x1536'
});
await fetch('https://ingest.fenra.io/usage/transactions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-Api-Key': process.env.FENRA_API_KEY
},
body: JSON.stringify({
provider: 'openai',
model: 'gpt-image-1.5',
usage: [{
type: 'images',
metrics: {
generated: response.data.length,
quality: 'high', // Options: 'low', 'medium', 'high'
width_px: 1024,
height_px: 1536
}
}],
context: {
billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
}
})
});
Audio (Whisper, TTS)
For Whisper transcription:
usage: [{
type: 'audio_seconds',
metrics: {
input_seconds: audioDurationInSeconds,
total_seconds: audioDurationInSeconds
}
}]
usage: [{
type: 'audio_seconds',
metrics: {
output_seconds: estimatedDuration,
total_seconds: estimatedDuration
}
}]
Supported Models
Fenra supports all OpenAI models. Common models include:
| Model | Type | Usage Type |
|---|
gpt-4o, gpt-4o-mini | Chat | tokens |
gpt-4-turbo, gpt-4 | Chat | tokens |
gpt-3.5-turbo | Chat | tokens |
o1-preview, o1-mini | Reasoning | tokens (with reasoning) |
o3, o3-mini | Reasoning | tokens (with reasoning) |
dall-e-3, dall-e-2 | Image | images |
whisper-1 | Audio | audio_seconds |
tts-1, tts-1-hd | Audio | audio_seconds |
Next Steps
